Method and System for Link Adaptation Using Metric Feedback

ABSTRACT

A method for link adaptation at a base station using metric feedback is disclosed. The method can include the steps of communicating with a mobile station over a communication link having one or more sub-channels and during the communication, receiving from the mobile station a plurality of mutual information per coded bit metrics for a plurality of candidate modulation types per coding layer. The mutual information per coded bit metrics can be based on channel state knowledge of the sub-channels. Also, based on the received information, the operational performance of the mobile station can be predicted in view of one or more transmission parameters and performance factors and one or more of the transmission parameters, including a modulation type selected from one of the plurality of candidate modulation types per coding layer, can be selected based on a transmission condition of the communication link.

RELATED APPLICATION

This application is related to U.S. patent application Ser. No.11/744,681 filed May 4, 2007 and entitled “Method and System for LinkAdaptation using Metric Feedback”.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention concerns link adaptation and more particularly,techniques for link adaptation based on mutual information per coded bitmetrics. 2. Description of the Related Art

Next generation cellular systems support multiple transmission modes,which can be used to improve the performance of such systems by adaptingto current channel conditions. This process is referred to as linkadaptation. Typically, these transmission modes include differentmodulation and coding schemes (MCS) and different multiple antennaarrangements—like beamforming, space-time coding and spatialmultiplexing—as the transmission becomes simultaneouslymulti-dimensional in space, time and frequency domain.

To achieve the system-level gain promised by link adaptation, a basestation requires the feedback of certain information from a mobilestation with which the base station is communicating. It is desirable,however, to limit the amount of feedback bits assigned to a feedbackchannel. As such there is a trade-off between the amount of feedback andthe performance improvements that can be achieved.

In one feedback technique, the mobile station selects the transmissionMCS and feeds this selection back to the base station. There are,however, several disadvantages with this approach. For example, the basestation may have a set of operational conditions (including packet size,target packer error rate, etc.) that are different from the assumptionsmade by the mobile station when the mobile station made its selections.Thus, the base station has no way of knowing if an MCS different fromthe one selected by the mobile station would be a better choice in viewof that operational condition. A second technique is employed in which amobile station feeds back to the base station three parameters, whichthe base station uses to calculate the effective signal-to-noise ratio(ESNR) for each transmission mode. This feedback method, however, isinefficient because it requires twenty-four bits of feedback and alsobecause the mobile station must request a feedback channel throughmedium access control (MAC) requests.

BRIEF DESCRIPTION OF THE DRAWINGS

The features of the present invention, which are believed to be novel,are set forth with particularity in the appended claims. The invention,together with further objects and advantages thereof, may best beunderstood by reference to the following description, taken inconjunction with the accompanying drawings, in the several figures ofwhich like reference numerals identify like elements, and in which:

FIG. 1 illustrates an example of a communication system;

FIG. 2 illustrates an example of operations occurring at a mobilestation;

FIG. 3 illustrates an example of operations occurring at a base station;

FIG. 4 illustrates an example of a predetermined table known to both amobile station and a base station;

FIG. 5 illustrates another example of a predetermined table known toboth a mobile station and a base station; and

FIG. 6 illustrates an example of the computation of a new metric.

DETAILED DESCRIPTION OF THE INVENTION

While the specification concludes with claims defining the features ofthe invention that are regarded as novel, it is believed that theinvention will be better understood from a consideration of thefollowing description in conjunction with the drawings, in which likereference numerals are carried forward.

As required, detailed embodiments of the present invention are disclosedherein; however, it is to be understood that the disclosed embodimentsare merely exemplary of the invention, which can be embodied in variousforms. Therefore, specific structural and functional details disclosedherein are not to be interpreted as limiting, but merely as a basis forthe claims and as a representative basis for teaching one skilled in theart to variously employ the present invention in virtually anyappropriately detailed structure. Further, the terms and phrases usedherein are not intended to be limiting but rather to provide anunderstandable description of the invention.

The terms “a” or “an,” as used herein, are defined as one or more thanone. The term “plurality,” as used herein, is defined as two or morethan two. The term “another,” as used herein, is defined as at least asecond or more. The terms “including” and/or “having,” as used herein,are defined as comprising (i.e., open language). The term “coupled” asused herein, are defined as connected, although not necessarilydirectly, and not necessarily mechanically. The term “processor” or“controller” can include any component or group of components, includingany relevant hardware and/or software, that can carry out the functionsdescribed in relation to the arrangements herein.

The term “mobile station” can be any portable component or group ofportable components that are capable of receiving and/or transmittingcommunications signals. A “base station” can be any infrastructurecomponent that is capable of exchanging wireless signals with a mobilestation and can, with appropriate feedback information, predict theperformance of the mobile station. A “communication link” can mean anymedium over which wireless signals may travel. A “transceiver” can meanany component or group of components that are capable or transmittingand/or receiving wireless signals over a suitable medium.

A method and system for link adaptation at a base station using metricfeedback is disclosed. The method can include the steps of communicatingwith a mobile station over a communication link having one or moresub-channels and during the communication, receiving from the mobilestation information relating to one or more mutual information per codedbit metrics for one or more candidate modulation types. The mutualinformation per coded bit metrics can be based on channel stateknowledge of the sub-channels. Also, based on the received information,the operational performance of the mobile station can be predicted inview of one or more transmission parameters and performance factors andone or more of the transmission parameters can be selected based ontransmission condition of the communication link.

A method of link adaptation at a mobile station is also described. Thismethod can include the steps of computing one or more mutual informationper coded bit metrics for one or more candidate modulation types basedon a channel state knowledge of one or more sub-channels and generatingfeedback information relating to the mutual information per coded bitmetrics for the candidate modulation types according to a request from abase station with which the mobile station is communicating. Thegenerated information can then be transmitted to the base station.

In view of these methods, efficient link adaptation can be performedwith minimal feedback overhead. In fact, the feedback information can betransmitted over existing or future feedback channels without requiringany exchange of MAC requests or assignment of additional channelsrequired for effective SNR metrics. Nonetheless, a base station is stillenabled to predict the performance of a mobile station based on thisefficient feedback metric to permit the base station to select one ormore transmission parameters.

Referring to FIG. 1, a communication system 100 is shown in which a basestation 110 is in wireless communications with a mobile station 120. Thebase station 110 can be part of any suitable communications network thatcan facilitate communications between the mobile station 120 and thecommunication network. As an example, the base station 110 and themobile station 120 can communicate with one another over a communicationlink that supports multiple transmission modes, as the transmissionsbetween the base station 110 and the mobile station 120 can besimultaneously multi-dimensional in space, time and frequency domains.

In one arrangement, the base station 110 can include a transceiver 130,a performance engine 140 and a memory 150 in which the engine 140 can becoupled to both the transceiver 130 and the memory 150. In addition, themobile station 120 can include a transceiver 160, a processor 170 and amemory 180 in which the processor 170 can be coupled to both thetransceiver 160 and the memory 180. As those of skill in the art willappreciate, the base station 110 and the mobile station 120 can exchangewireless signals over the communication link through the transceivers130 and 160. As part of this exchange, the mobile station 120 can feedback to the base station 110 information relating to one or more mutualinformation per coded bit metrics for one or more candidate modulationtypes, which can be generated by the processor 170. As part of thisgeneration, the processor 170 may access relevant information from thememory 180.

In response, the performance engine 140 can—by accessing relevantmaterial from the memory 150—predict the operational performance of themobile station in view of one or more transmission parameters andperformance factors. The performance engine 140 can then select one ormore of the transmission parameters based on a transmission condition ofthe communication link, which enables the process of link adaptation.

In another arrangement, the processor 170 can computing one or more MIBmetrics for one or more candidate modulation types based on a channelstate knowledge of one or more sub-channels. The processor 170 can alsogenerate feedback information based on the MIB metrics for the candidatemodulation types according to a request from the base station 110.Further discussion relating to these processes will be presented below.

Referring to FIG. 2, a method 200 for link adaptation at a mobilestation is shown. This method 200 focuses on the processes that canoccur at the mobile station 120 of FIG. 1, particularly if the basestation 110 supports single-output/single-input (SISO) orsingle-input/multiple output (SIMO) operation, although it must beunderstood that the method 200 can be practiced in any other suitablesystem or component using any other suitable transmission mode ortechnique. The steps of the method 200—or any other method presentedbelow—are not limited to the particular order in which they arepresented in the figures. Moreover, any of these methods can have agreater number of steps or a fewer number of steps than those shown inthe figures.

At step 210, a communication link can be established between, forexample, the base station 110 and the mobile station 120 in which thebase station supports SISO or SIMO operation. This communication linkcan include one or more sub-channels, and as noted earlier, can bemulti-dimensional in nature. At step 215, the base station 110 canrequest a particular type of feedback (to be described below) to be sentfrom the mobile station 120 and can assign a feedback channel. As anexample, the base station 110 can assign a six-bit fast feedbackchannel, such as that found in the 802.16e standard.

At step 220, the mobile station 120, for example, can determine one ormore mutual information per coded bit (MIB) metrics for one or morecandidate modulation types. A candidate modulation type is a possiblemodulation scheme that can be employed between a base station and amobile station over a wireless link. Suitable examples, as will bedescribed later, include quadrature phase shift keying (QPSK) 16-bitquadrature amplitude modulation (16-QAM) and 64-bit quadrature amplitudemodulation (64-QAM), although other modulation schemes are within thescope of the claimed subject matter.

The number of MIB metrics computed at step 220 generally depends on, forexample, the number of candidate modulation types, but it could alsodepend on the feedback type that the base station requested. As anexample, the base station 110 can request the MIB metric for aparticular modulation type (e.g., 64QAM) or all modulation types.Alternatively, the base station 110 could request the mobile station 120to recommend a preferred modulation type along with the associated MIBmetric, in which case the mobile station 120 may choose to compute theMIB metrics for all modulation types, even though it only feeds backone. In another example, the base station 110 could request just thepreferred MCS, which contains the information of a preferred modulationtype and one of the allowed coding rates for that modulation type. Suchan MCS request mechanism is specified in the current IEEE 802.16estandard.

The MIB determination can be based on, for example, channel stateknowledge of the sub-channels. In a typical arrangement, the mobilestation 120 can estimate the channel state knowledge, which refers tothe channel responses at the sub-channels, using certain referencesignals transmitted from the base station 110. The channel responsescontain the amplitude and phase information of the channels between eachof the transmission antennas at the base station 110 and each of thereceive antennas at the mobile station 120. Therefore, the channel stateknowledge at a sub-channel can be a complex scalar in a SISO case or acomplex vector in SIMO or multiple-input/single-output (MISO) cases. Thechannel state knowledge can also be a complex matrix inmultiple-input/multiple-output (MIMO) transmission.

Depending on the transmission scheme, there may be different receivertypes. Hence, the MIB metrics computed at step 220, in addition to beingfor candidate modulation types, can be related to a receiver type. Areceiver type can be defined as a receiving processing method in lightof a particular transmission scheme, such as SIMO or MIMO. For a giventransmission scheme, multiple possible processing methods may exist. Forexample, for MIMO matrix-B mode in IEEE802.16e, a maximal likelihood(ML) or a minimum mean squared error (MMSE) receiver processing can beused.

The MIB metric can be the mutual information between coded bits andcorresponding bit LLRs (Log-Likelihood Ratio) input to a binary decoder.The coded bits are from the binary encoder that precedes the mapping ofbits into symbols according to a modulation type (e.g., BPSK, QPSK,16QAM, 64QAM, etc.). The symbols are then sent from the base station 110according to the transmission schemes, and after the symbols aretransmitted over the channel, the receiver will compute the bit LLR ofeach coded bit based on the estimated channel knowledge. The bit LLR canbe the output of a receiver processing and LLR computation engine. If“bit channels” are used to refer to the total effect between the inputto modulation mapping and the output of the receiver processing and LLRcomputation engine, each bit comprising the modulation (e.g., 4 bitscomprising each 16QAM symbols) experiences its own bit channel. The MIBmetric can be referred to as the mutual information of those bitchannels.

In one arrangement, the MIB metric can have a value between 0 and 1 andcan reflect an ideal coding rate that can be approximately supported bya transmitter, if there is an ideal encoder and decoder. As an example,for SISO operation, the MIB can be expressed by the following formula:

${I\left( {b,{LLR}} \right)} = {\frac{1}{m}{\sum\limits_{i = 1}^{m}{I\left( {b_{i},{{LLR}\left( b_{i} \right)}} \right)}}}$

where ^(I(b) ^(i) ^(, LLR((B) ^(i) ⁾⁾ is the mutual information betweenthe coded bit and the LLR for ^(i) ^(th) bit of a modulation type. As anexample, there are m=2, 4 or 6 bits corresponding to QPSK, 16QAM, and64QAM modulation, respectively. The mobile station 120 can compute theMIB metric for one or more of the modulation types, according to theneed to satisfy the request from the base station 110. For example, thebase station 110 can request the MIB just for a particular modulationtype (e.g., m=6), or the base station 110 may request the mobile station120 to recommend a preferred modulation type, in which case the mobilestation 120 may choose to compute the MIB for all modulation types inorder to make a selection.

Referring again to step 220, during the computation of the MIB metricfor one or more candidate modulation types, the mobile station maycompute the MIB metric on at least one of the sub-channels. The mobilestation 120 can then average the MIB metric over one or more of thesub-channels. The above ^(I(b) ^(i) ^(,LLR(b) ^(i) ⁾⁾ function can becomputed on a sub-channel basis. When the communication is over achannel comprising multiple sub-channels, as described earlier, the MIBmay need to be averaged over a number of sub-channels to reflect theoverall communication link quality.

In some cases, however, where the channel state knowledge or thequalities of sub-channels are similar, the MIB in one sub-channel may besufficient. Also note that it is not necessary to compute the MIB on allthe sub-channels. As an example, a subset of all sub-channels can beused. The MIB metric computed by the mobile station 120 could be moreprecisely referred to as mean MIB or MMIB in this case, but forconvenience and clarity, the term mutual information per coded bits orMIB is used in both cases, i.e., with or without averaging.

Considering another aspect of the computation of the MIB metrics, themobile station 120 may first derive one or more variables based on thechannel state knowledge and a receiver type. In the case of SISO/SIMOtransmission, a single variable could be derived, which is often someform of Signal to Noise and Interference Ratio (SINR), which can beeasily derived form the channel state knowledge and the receiver type,as is well known in the art. The SINR variable is appropriate torepresent channel condition on a subchannel and the SINR typicallyshould represent the SINR after a certain receiver processing, i.e., toreflect the quality of a subchannel accounting for a specific receivertype.

For MIMO transmission, one or more variables could be derived, based onone or more of the following parameters: eigenvalues, eigen-subspacepowers, and a condition number of a channel state matrix. Other suitableparameters may be applicable here.

As another part of the computation of the MIB metrics, the mobilestation 120 can map the variables to an MIB value based on apredetermined numerical table. As an example, the table, which can storea set of MIB values corresponding to a set of variable values, can beconstructed based on numerical simulation.

As another embodiment, the MIB metric can also be approximated as afunction parameterized by one or more variables. For example, thefunction can be a linear combination of certain “basis” functions, eachof which can be parameterized by one or more variables. For example, inthe case of SISO or SIMO, one variable is needed, which may be the SINRof the sub-channel. The MIB metric in this case is shown below:

${I(x)} = {\sum\limits_{i = 1}^{K}{a_{i}{J\left( {c_{i}\gamma} \right)}}}$${\sum\limits_{i}a_{i}} = 1$

where c₁ . . . c_(K) are pre-computed scalars, γ is the SINR of thesub-channel, and the MIB metric is a weighted sum (by scalar “a_(i)”) ofK basis functions (i.e., J function).

A basis function may correspond to the MIB metric of a binary phaseshift keying (BPSK) modulation (denoted as “J” function above). The Jfunction is known in the art and is a nonlinear function but has aclosed-form expression for computational purposes. It is also possibleto store a numerical table for the whole function should one choose theapproach to compute J function.

The particular example of using a function built upon a J basis functionand parameterized by the variables can be obtained by approximating aProbability Distribution Function (PDF) of a conditional LLR as amixture of Gaussian distributions, which is shown in the followingformula:

${{{Mixture}\mspace{14mu} {of}\mspace{14mu} {Gaussians}}->{I(x)}} = {\sum\limits_{i = 1}^{K}{a_{i}{J\left( {c_{i}x} \right)}}}$${\sum\limits_{i}a_{i}} = 1$

As an example, the equation below provides numerically-derived functionsto obtain the MIBs for three different modulation types given the SNR ona subchannel. The parameterized functions are found to be a goodapproximation of the MIB metrics. In general, the approximations dependon the specific constellation mappings for each supported modulationtype.

${I_{2}(\gamma)} = {J\left( {2\; \sqrt{\gamma}} \right)}$${I_{4}(\gamma)} = {{\frac{1}{2}{J\left( {0.8\sqrt{\gamma}} \right)}} + {\frac{1}{4}{J\left( {2.17\; \sqrt{\gamma}} \right)}} + {\frac{1}{4}{J\left( {0.965\sqrt{\gamma}} \right)}}}$${I_{6}(\gamma)} = {{\frac{1}{3}{J\left( {1.47\sqrt{\gamma}} \right)}} + {\frac{1}{3}{J\left( {0.529\sqrt{\gamma}} \right)}} + {\frac{1}{3}{J\left( {0.366\sqrt{\gamma}} \right)}}}$

Information relating to the MIB metrics can be sent from the mobilestation 120 to the base station 110, and there are several ways thatthis can be performed, as described earlier. For example, at decisionstep 225, it can be determined whether the base station 110 hasrequested a process of modulation-coding scheme (MCS) feedback or MIBfeedback. In summary, an MCS feedback includes the mobile station 120selecting the modulation type and forwarding it and other relevantinformation to the base station 110 over the feedback channel.Conversely, the MIB feedback model involves the mobile station 120 notselecting the modulation type but forwarding information (along withother data) to the base station 110 to allow the base station 110 toselect the modulation type. It is also possible for the base station 110to request a combination of both types of feedback here. As an example,the preferred modulation type and the associated MIB can be requested.

If the MCS feedback is requested, at step 230, the operationalperformance of the mobile station 120 can be predicted, for example,corresponding to candidate modulation types and coding rates, usingnumerically derived-to-error rate relationships or parameterizedmetric-to-error rate relationships, as described earlier. Thisparticular process can be conducted at the mobile station 120. Thecandidate modulation types and the coding rates can be referred to astransmission parameters, while an error rate can refer to a performancefactor. A transmission parameter can be defined as any parameter thataffects the characteristics of a transmission, while a performancefactor can be any factor whose affect on a transmission can be measured.It is understood, however, that a transmission parameter is not limitedto modulation types or coding rates and that a performance factor is notlimited to an error rate.

Focusing on step 230, the prediction of operational performance, usingMIB metrics for one or more candidate modulation types, can use afunctional relationship between an MIB metric and an error rate.Consider, for example, a Gaussian cumulative model to approximate thefunctional relationship using three parameters:

${y = {\frac{a}{2}\left\lbrack {1 - {{erf}\left( \frac{x - b}{\sqrt{2}c} \right)}} \right\rbrack}},{c \neq 0}$

where “erf” is an error function well known in the art, a is atransition height, b is a transition center and c is related to atransition width (transition width is equal to 1.349c) of a Gaussiancumulative distribution. In a linear packet error rate (PER) domain, theparameter a can be set to one, and the mapping only requires twoparameters, which can be pre-computed and stored for supportedmodulation types, code rates and packet sizes. A further simplificationto the above functional relationship is to make it independent of themodulation type.

As another method to predict the performance using MIB metrics, anumerically derived MIB-to-error rate relationship can be used, i.e.,using a lookup table to perform the error-rate mapping, where a set oferror rates are stored corresponding to a set of MIB metrics, and theerror rate for a metric is obtained by well-known linear interpolationmethods.

Once the performance is predicted, at step 235 of FIG. 2, thetransmission parameters that satisfy predetermined performancerequirements can be chosen and fed back from the mobile station 110 tothe base station. For example, predetermined performance requirementscan be a target PER rate for a certain packet size. The base station 110may also signal these requirements to the mobile station 120 whensetting up at the connection. A description of what the base station 110can do with this feedback information is recited below.

Moving back to decision step 225, if the base station 110 has requestthe MIB feedback technique, at step 240, feedback information can begenerated by, for example, mapping MIB metrics to an index. In onearrangement, this mapping can be according to a predetermined mappingtable known to the base station 110 and to the mobile station 120. Inaddition, the generation of this feedback information can occur at themobile station 120. At step 245, the feedback information can be sentover the assigned feedback channel, such as from the mobile station 120to the base station 110.

Moving back to decision step 225, if the base station 110 has requestthe MIB feedback technique, at step 240, feedback information can begenerated by, for example, mapping MIB metrics to an index. In onearrangement, this mapping can be according to a predetermined mappingtable known to the base station 110 and to the mobile station 120. Inaddition, the generation of this feedback information can occur at themobile station 120. At step 245, the feedback information can be sentover the assigned feedback channel, such as from the mobile station 120to the base station 110.

The construction of a predetermined mapping known to both the basestation and the base station will be described now. In particular, thecomputed one or more MIB metrics is mapped to an index to an entry of apredetermined mapping table, which can include a plurality of indices.The table can be constructed based on certain system parameters thatenable efficient transmission of the feedback information using a smallnumber of feedback bits. When the MIB metrics are mapped to thepredetermined mapping table, an index of the mapping table can beselected, which can serve as the feedback information. In onearrangement, the number of indices in the predetermined mapping tablecan have a value of 2^(b), where b is the size of the feedback channel.As an example, the 802.16(e) standard allows for a fast feedback channelthat supports six bit feedback. In this case, the number of indices inthe mapping table can equal 2⁶, or sixty-four indices.

In addition to the processes described in relation to method 200, thereare several other alternatives to consider. For example, a communicationlink may be established between the base station 110 and the mobilestation 120 in which the base station 110 supports multiple MIMOtransmission, such as open-loop MIMO or closed-loop MIMO operation.There are additional transmission parameters that are relevant to MIMOoperation, such as the open-loop and closed-loop MIMO transmission modeinformation including, but not limited to, a number of symbol streams tobe sent, a number of coding layers, and the closed-loop MIMOtransmission mode information further includes closed-loop antennabeamforming weights of each symbol stream (also referred to as“pre-coding” weights).

In this case, the base station 110 may request feedback under aparticular MIMO transmission mode. For example, only the MCS feedbackfor a particular open-loop MIMO scheme may be requested (e.g., matrix-Aor matrix-B as specified in the IEEE 802.16e standard). As such, using apotential transmission mode and certain parameters, a modified channelquality can be obtained at the mobile station 120, such as the SINRafter considering the receiver processing. The mobile station 120, basedon the requested MIMO transmission mode, can then compute MIB metricsfor one or more candidate modulation types per coding layer. Once theMIB metrics are computed, the rest of the operation at the mobilestation is similar to the SISO/SIMO processes described in relation tomethod 200. Specifically, relevant information can be fed back from themobile station 120 to the base station 110, depending on which feedbacktechnique the base station 110 has requested.

In another arrangement, the base station 110 may request feedback ofrecommended MIMO transmission parameters. Specifically, the mobilestation 120 can compute MIB metrics for one or more candidate modulationtypes and candidate MIMO transmission modes and can use these MIBmetrics to determine one or more performance factors with different MIMOtransmission parameters. The mobile station 120 can then select apreferred set of such transmission parameters. For this operation, themobile station 120 can use a parameterized relationship or a numericallyderived look-up table relationship to predict the performance based onMIB metrics, as discussed earlier. The mobile station 120 can then feedback to the base station the selected MIMO transmission parameters, alsoin accordance with the discussion above.

As noted earlier, the computation of the MIB metrics may be related toreceiver type. For example, the receiver type may be a SISO/SIMOreceiver or a MIMO linear receiver (e.g., MMSE). In this case, SNRs canbe derived taking into account the transmission mode and the receivertype.

A maximum likelihood MIMO receiver is another receiver type. In thiscase, the MIB metric computation can be modified as follows. A set ofGaussian means corresponding to the mixture Gaussian distribution can bederived based on the MIMO channel matrix on the sub-channel (matrixentries correspond to the channel form each transmit to receiveantenna). In particular, the Gaussian means are derived based on one ormore variables relating to the channel condition. For example, thevariables can be the eigenvalues, eigen subspace powers, and a conditionnumber of the channel state matrix.

As an example, eigen values and eigen subspace powers can be obtainedfor a 2 transmit antenna and a 2 receive antenna channel. A first stepperforms an Eigen value decomposition of the channel matrix:

H ^(H) H =VDV ^(H)

where V is the matrix consisting of eigenvectors and D is a diagonalmatrix containing the two eigenvalues. From this decomposition, thefollowing three variables can be obtained, which can then be used toobtain conditional means as described above:

λ_(min)—Minimum Eigen Value

λ_(max)—Maximum Eigen Value

p_(a)—Eigen Mode Power distribution parameter=min{p,1−p}

${{{where}\mspace{14mu} {{V} \cdot {V}}} = \begin{pmatrix}p & {1 - p} \\{1 - p} & p\end{pmatrix}},{0 \leq p \leq 1}$

The MIB metric is then derived by using a sum of J approximation, asfollows:

H− > [γ₁, γ₂, γ₃, …  , γ_(K)]${{I(H)} = {\sum\limits_{i = 1}^{K}{a_{i}{J\left( {c_{i}\gamma_{i}} \right)}}}},{{a_{1} + a_{2} + a_{3} + \ldots + a_{K}} = 1}$

If more than one coding layer is supported, corresponding number ofmetrics are obtained with a similar approach.

Referring to FIG. 3, a method 300 of link adaptation at the base station110 using feedback metrics is shown. The method 300 is an example of howthe base station 110 can process the feedback information that the basestation 110 receives from the mobile station 120. The method 300 alsoapplies to both SISO/SIMO and MIMO transmission modes, as will bedescribed. Similar to the method 200 of FIG. 2, at step 310, acommunication link can be established with the mobile station 120, andat step 315, the base station 110, for example, can request a feedbacktype and can assign a feedback channel. The base station 110 can receivethe feedback information from the mobile station 120, as shown at step320.

As noted earlier, the base station 110 may request either of or acombination of two types of feedback information from the mobile station120, an MCS feedback scheme and an MIB feedback process. The basestation 110 can directly use the MCS feedback information. If MIBfeedback information is sent to the base station 110, then at step 325,the base station 110, based on either one of these techniques, canrecover MIB metrics of one or more candidate modulation types from thefeedback information, which can be based on a predetermined mappingtable known to the base station 110 and the mobile station 120. Thispredetermined mapping table can be the same mapping table described inrelation to the operation described above for the mobile station 110.The design of an example of such a table will be discussed in thissection.

In an embodiment of the method, based on a pre-determined table known toboth the base station and the mobile station, the MIB metrics of one ormore candidate modulation types can be mapped to an index (feedbackinformation). The index can be transmitted on the assigned feedbackchannel and can correspond to one of the 2^(b) indices supported by ab-bit feedback channel. In one arrangement, the index may contain theinformation of a single preferred modulation type using, for example, 2bits of the b bits, and using the rest of bits to convey the quantizedmetric value corresponding to that modulation type. Finer quantizationof the metric can be obtained if more bits can be used.

Finer quantization can also be achieved by reducing the quantizationrange of the metric. It is possible to reduce the range based on thesupported coding rates at the base station. A range that is slightlylarger than the allowable coding rate range is often sufficient. Forexample, the coding rates and the corresponding modified metric rangesare shown below for several modulation types:

QPSK→[0.1-0.85] (R_(min)= 1/12,R_(max)=¾)

16QAM→>[0.5-0.85] (R_(min) =½,R _(max)=¾)

64QAM→[0.55-1] (R_(min)=½, R_(max)=⅚)

Although not intended to be limiting, a particular example of thepredetermined table is illustrated in FIG. 4, constructed based on themethods described above, where reduced quantization ranges of[0,1],[0.5,1],[0.5,1] are used for metrics corresponding to QPSK, 16QAMand 64QAM, respectively.

In another embodiment, the three MIB metrics as a group may be mapped toa feedback index according to a table where each entry can represent agroup of three metrics (each with a particular value). To reduce thenumber of feedback bits, the joint probability distribution of themetrics can be observed in the construction of such table. The jointprobability distribution can represent the probabilities of the MIBmetrics taking on particular respective values. The distribution may beobtained via numerical simulation. By choosing 2^(b) sets of values thathave the largest sum probability of occurrence (referred to as typicalset below), the feedback can be limited to b bits, while still providingaccurate feedback in all practical situations.

${{Typical}\mspace{14mu} {Set}} = {\underset{\{{i_{i},i_{2},\mspace{14mu} \ldots \mspace{14mu},i_{2^{b}}}\}}{argmax}{\sum\limits_{j = 1}^{2^{b}}{P\left( i_{j} \right)}}}$

Additional constraints can be observed to further to limit the range ofMIBs for each candidate modulation type. Such additional constraintscould include the allowable coding rates under each modulation type asdefined in a standard.

A particular example of a predetermined table, constructed based on theapproach described above, is shown in FIG. 5, for M=40 quantizationlevels and b=6 bits. Constraints on coding rates are also consideredhere. Each of the 64 entries of the table contains a group of 3 MIBmetrics corresponding to modulation types QPSK, 16QAM and 64QAM. Eachentry (i.e., a set of three MIB metrics) is associated with an index of6 bits, for example, according to the entry's index position in thetable. The mobile station 120 can compute the MIB metrics for one ormore candidate modulation types (three MIBs in the example of FIG. 5)and can select an entry where the associated MIB metrics best match thecomputed MIB metrics, according to certain criteria, such as minimalsquared distance over the three metrics.

The base station 110 may include HARQ as part of its scheduling. As isknown in the art, in the event of the receipt of a faulty packet, HARQpermits the combining of the faulty packet with a subsequent packet,which can increase the chances of a successful transmission. As is alsoknown in the art, one type of HARQ retransmission is a simple repetitionof the first transmitted packet, and the associated combining iscommonly referred to as chase combining. Another well known type of HARQoperation is incremental redundancy (IR), where subsequent packets maynot have the same coded bits as the previous packets. If eachre-transmission is not individually decodable, then combining isrequired at the mobile station 120. Otherwise, a mobile station 120 candrop the previous packets and not necessarily perform HARQ. Thus, atdecision step 330, it can be determined whether a packet to betransmitted from the base station 110 to the mobile station 120 is to becombined with previously transmitted packet.

If the combination is to occur, then the base station 110 may need todetermine the transmission parameters of the subsequent packet. In orderto do that, the base station 110 can first compute a new MIB metricbased on the MIB metrics received during different time instantscorresponding to first and subsequent packet re-transmissions, as shownat step 335.

For example, as illustrated in FIG. 6, the new mutual information metriccan be computed as

${MIB}_{HARQ} = \frac{\sum\limits_{i = 1}^{N}{c_{i}{MIB}_{i}}}{\sum\limits_{i = 1}^{N}c_{i}}$

where c_(i) is the number of coded bits in the i-th transmission andMIB_(i) is the mutual information per coded bit metric corresponding tothe i-th transmission. Further, the metric to error rate mapping (step340 as will be described in detail later) can take into account a neweffective code rate and a new code size to reflect combined decoding.For example, if the information packet size has “x” bits, then we canhave (“p” transmissions are assumed in the equation):

${{Effective}\mspace{14mu} {Code}\mspace{14mu} {Rate}} = \frac{x}{c_{1} + c_{2} + {\ldots \mspace{14mu} c_{P}}}$Effective  Code  Size = c₁ + c₂ + …  c_(P)

After step 335 or if no combination of transmissions will occur, themethod 300 resumes at step 340, where the base station 110 can predictthe performance of the mobile station 120 in view of differenttransmission parameters, using numerically determined or parameterizedrelationships between an MIB and a packet error rate. For example, thetransmission parameters may include modulation type, coding rate andpacket size. Such numerical and parameterized relationships have beenexplained before in detail with regard to similar operation at themobile station 120.

A variation of the above method for MIMO transmission, in which a basestation 110 may prefer a particular MIMO transmission mode and hencerequest metric feedback for this particular mode, is now described. Abase station 110 can also request multiple metrics for multiplecandidate MIMO modes, via assigning multiple feedback channels. Inaddition, if a requested MIMO mode has multiple coding layers, MIBmetric feedback can be requested for each coding layer. The per-layerMIB metrics can then be used to select transmission parameters, likemodulation type, coding rate and packet size based on a predictedperformance using parameterized or numerically determined relationshipbetween an MIB and a packet error rate.

In another arrangement, the base station 110 may request the mobilestation 120 to also feedback a recommendation of MIMO transmission modeinformation. Such MIMO transmission mode information can include openloop or closed loop MIMO transmission schemes, number of coding layers,number of symbol streams and antenna beamforming weights vector (alsoknown as “precoding” vector), in the case of closed-loop MIMOtransmission. The base station 110 can then receive a mobile recommendedMIMO transmission mode and the corresponding MIB metrics and can selectthe appropriate MCS for each coding layer, as previously explained.

The metric in this specification is referred to as MIB. Further it isunderstood that mutual information per bit (MIB) is computed for asub-channel and the MIB metric when averaged over more than onesub-channels can be referred to as mean MIB (MMIB). For convenience, MIBis used in all situations.

While the preferred embodiments of the invention have been illustratedand described, it will be clear that the invention is not so limited.Numerous modifications, changes, variations, substitutions andequivalents will occur to those skilled in the art without departingfrom the spirit and scope of the present invention as defined by theappended claims.

What is claimed is:
 1. A method for link adaptation at a base stationusing metric feedback, comprising: communicating with a mobile stationover a communication link having one or more sub-channels; during thecommunication, receiving from the mobile station information a pluralityof mutual information per coded bit metrics for a plurality of candidatemodulation types per coding layer, wherein the mutual information percoded bit metrics are based on channel state knowledge of thesub-channels; based on the received plurality of mutual information percoded bit metrics, predicting operational performance of the mobilestation in view of one or more transmission parameters and one or moreperformance factors; and selecting one or more of the transmissionparameters, including a modulation type selected from one of theplurality of candidate modulation types per coding layer, based ontransmission condition of the communication link.
 2. The methodaccording to claim 1, wherein the transmission parameters furtherinclude open-loop multiple-input multiple-output (MIMO) transmissionmode information or closed-loop MIMO transmission mode information, anda coding rate or a packet size for the coding layers of the open-loopand closed-loop MIMO transmission modes.
 3. The method according toclaim 2, wherein the open-loop MIMO transmission mode information andthe closed-loop MIMO transmission mode information include a number ofsymbol streams to be sent or a number of coding layers and theclosed-loop MIMO transmission mode information further includesclosed-loop antenna beamforming weights of each symbol stream.
 4. Themethod according to claim 1, wherein the plurality of mutual informationper coded bit metrics for the plurality of candidate modulation typesper coding layer is an index transmitted from the mobile station.
 5. Themethod according to claim 4, wherein the index is mapped into a bitfield.
 6. The method according to claim 4, wherein the index is based ona predetermined table known to both the base station and the mobilestation.
 7. The method according to claim 6, wherein the predeterminedtable is defined based on different quantization ranges for mutualinformation per coded bit metrics of each candidate modulation type percoding layer.
 8. The method according to claim 6, wherein thepredetermined table known to both the base station and the mobilestation is defined based on joint probability distribution of theplurality of mutual information per coded bit metrics and coding rateconstraints for each candidate modulation type per coding layer.
 9. Themethod according to claim 1, wherein predicting operational performanceof the mobile station in view of one or more transmission parameters andperformance factors comprises: mapping the mutual information per codedbit metric corresponding to a candidate modulation type and a codinglayer to a packet error rate under potential coding rates and packetsizes according to a numerically determined relationship between themutual information per coded bit metric and the packet error rate oraccording to a parameterized relationship.
 10. The method according toclaim 9, wherein the parameterized relationship is defined as:${y = {\frac{a}{2}\left\lbrack {1 - {{erf}\left( \frac{x - b}{\sqrt{2}c} \right)}} \right\rbrack}},{c \neq 0}$wherein “y” is the packet error rate, “x” is the mutual information percoded bit metric corresponding to a candidate modulation type, “erf” isan error function, and “a”, “b”, and “c” are stored parameters.
 11. Amethod for link adaptation at a mobile station, comprising: computing aplurality of mutual information per coded bit metrics for a plurality ofcandidate modulation types per coding layer based on a channel stateknowledge of one or more sub-channels; generating feedback information,wherein the feedback information includes the plurality of mutualinformation per coded bit metrics for the plurality of candidatemodulation types per coding layer according to a request from a basestation with which the mobile station is communicating; and transmittingthe generated feedback information to the base station.
 12. The methodaccording to claim 11, wherein computing the plurality of mutualinformation per coded bit metrics for the plurality of candidatemodulation types per coding layer comprises: for each candidatemodulation type, computing the mutual information per coded bit on atleast one of the sub-channels as the mutual information between eachcoded bit and a log-likelihood ratio of that bit based on a receivertype; and averaging the mutual information per coded bit over one ormore of the subchannels.
 13. The method according to claim 11, whereincomputing the mutual information per coded bit metric on a subchannelcomprises: deriving one or more variables relating to a channelcondition based on the channel state knowledge and a receiver type; andmapping the one or more variables to the mutual information per codedbit metric.
 14. The method according to claim 13, wherein mapping thevariables to the mutual information per coded bit is based on apredetermined numerical table or a function parameterized by thevariables.
 15. The method according to claim 11, wherein the feedbackinformation is an index that the mutual information per coded bitmetrics are mapped to based on a predetermined table known to the basestation and the mobile station or is a recommended setting for one ormore transmission parameters as requested by the base station or is acombination of both.
 16. A base station for link adaptation that usesmetric feedback, comprising: a transceiver that communicates with amobile station over a communication link having a plurality ofsub-channels and, during the communication, receives from the mobilestation information a plurality of mutual information per coded bitmetrics for a plurality of candidate modulation types per coding layer,wherein the mutual information per coded bit metrics are based onchannel state knowledge of the sub-channels; and a performance enginecoupled to the transceiver, wherein based on the received plurality ofmutual information per coded bit metrics, the performance enginepredicts operational performance of the mobile station in view of one ormore transmission parameters and performance factors and selects one ormore of the transmission parameters, including a modulation typeselected from one of the plurality of candidate modulation types percoding layer, based on transmission condition of the communication link.17. The base station according to claim 16, further comprising: a memorycoupled to the performance engine for storing a predetermined table,wherein the plurality of mutual information per coded bit metrics forthe plurality of candidate modulation types per coding layer referencesan index in the predetermined table.
 18. The base station according toclaim 17, wherein the predetermined table is known to both the basestation and the mobile station and is defined based on joint probabilitydistribution of the plurality of mutual information per coded bitmetrics and coding rate constraints of each of the candidate modulationtypes per coding layer.
 19. The base station according to claim 16,wherein the performance engine predicts operational performance of themobile station in view of one or more transmission parameters andperformance factors by mapping the mutual information per coded bitmetric corresponding to a candidate modulation type to a packet errorrate under potential coding rates and packet sizes according to anumerically determined relationship between the mutual information percoded bit metric and the packet error rate.
 20. A mobile station forlink adaptation using metrics, comprising: a transceiver forcommunicating with a base station; and a processor coupled to thetransceiver, wherein the processor: computes a plurality of mutualinformation per coded bit metrics for a plurality of candidatemodulation types per coding layer based on a channel state knowledge ofone or more sub-channels; and generates feedback information, whereinthe feedback information includes the plurality of mutual informationper coded bit metrics for the candidate modulation types per codinglayer according to a request from the base station, wherein thetransceiver transmits the generated feedback information to the basestation.
 21. The mobile station according to claim 20, wherein theprocessor computes the plurality of mutual information per coded bitmetrics by: computing the mutual information per coded bit on at leastone of the sub-channels as the mutual information between each coded bitand a log-likelihood ratio of that bit based on a receiver type; andaveraging the mutual information per coded bit over one or more of thesubchannels.
 22. The mobile station according to claim 20, furthercomprising: a memory, coupled to the processor, for storing apredetermined table known to the base station and the mobile station,wherein the feedback information has an index to the predetermined tableindicating the plurality of mutual information per coded bit metrics forthe plurality of candidate modulation types per coding layer.